Given a query of "transfer learning for video tagging" and a collection of the following three documents:

Use the Vector Space Model, TF$/$IDF weighting scheme, and Cosine vector similarity measure to find the most relevant document$($s$)$ to the query.

a$)$ If the support threshold is $6,$ which items are frequent? Calculate"document frequency" and "inverse document frequency" for each word.

b$)$represent each document as a weighted vector by using TF$/$IDF weight scheme. Length Normalization is not required.

c$)$ represent the query as a weighted vector and find its modet relevant document$($s$)$ using cosine similarity measure.Given a query of "transfer learning for video tagging" and a collection of the

following three documents:

Use the Vector Space Model, TF$/$IDF weighting scheme, and Cosine vector

similarity measure to find the most relevant document$($s$)$ to the query. Assume

that $"$a$","$on$",$ "for", "from" and $"$to$"$ are stop words.

The formula of TF$/$IDF Weighting is: $w_{ij}=t_{ij}log(\frac{N}{n_j})$

where:

$t_{ij}$ : the number of times term $j$ appeared in document $i.$

$N$ : the Total number of document.

$n_j$: the number of documents that term $j$ appears in

a$)$ If the support threshold is $6,$ which items are frequent? Calculate

"document frequency" and "inverse document frequency" for each word.

$[6$ points$]$

$($Hint: $log2=0\mathrm{.}301,log3=0\mathrm{.}477)$